The present invention pertains to a scaler scoring system for determining the miss distance of a projectile from a target, such as a remotely operated drone. The scaler scoring system is a training and weapons evaluation tool which must operate not only in the presence of thermal noise, but must also deal with problems such as sea clutter, interfering signals, target generated noise, partial antenna blockage, and complex projectiles with multiple scatterers. In addition, the scoring system must accommodate an extremely diverse set of scoring encounters, including a great number of different projectiles, a large scoring volume and a wide range of relative velocities.
Detecting a scoring encounter reliably in a clutter and noise contaminated environment without also generating extraneous data due to false alarms is a difficult problem and has major impact on scoring accuracy. This is a result of the fact that the accuracy of any computational algorithm is only as good as the input data provided. Many scoring algorithms, especially those that depend on least squares polynominal approximation, are seriously degraded by extraneous data points such as those introduced by radar false alarms. Experience has shown that the inaccuracies introduced by these false data points far outweigh the inaccuracies introduced by missing some of the available data. Even if accuracy is not lost due to false detections, valuable processing time is wasted in the scoring computer when false scores are detected and processed. These false detections interfere with processing real scores in a timely manner and lead to confusion as to which score reports are valid.